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Update new reference link of xpu/docker/readme.md (#12188)

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* [ADD] rewrite new vllm docker quick start

* [ADD] lora adapter doc finished

* [ADD] mulit lora adapter test successfully

* [ADD] add ipex-llm quantization doc

* [Merge] rebase main

* [REMOVE] rm tmp file

* [Merge] rebase main

* [ADD] add prefix caching experiment and result

* [REMOVE] rm cpu offloading chapter

* [ADD] rewrite new vllm docker quick start

* [ADD] lora adapter doc finished

* [ADD] mulit lora adapter test successfully

* [ADD] add ipex-llm quantization doc

* [Merge] rebase main

* [REMOVE] rm tmp file

* [Merge] rebase main

* [ADD] rewrite new vllm docker quick start

* [ADD] lora adapter doc finished

* [ADD] mulit lora adapter test successfully

* [ADD] add ipex-llm quantization doc

* [Merge] rebase main

* [REMOVE] rm tmp file

* [Merge] rebase main

* [UPDATE] update the link to new vllm-docker-quickstart
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Jun Wang 2024-10-18 13:18:08 +08:00 committed by GitHub
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docker/llm/serving/xpu/docker/README.md
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@ -69,7 +69,7 @@ You can modify this script to using fastchat with either `ipex_llm_worker` or `v
#### vLLM serving engine #### vLLM serving engine
To run vLLM engine using `IPEX-LLM` as backend, you can refer to this [document](https://github.com/intel-analytics/ipex-llm/blob/main/python/llm/example/GPU/vLLM-Serving/README.md). To run vLLM engine using `IPEX-LLM` as backend, you can refer to this [document](https://github.com/intel-analytics/ipex-llm/blob/main/docs/mddocs/DockerGuides/vllm_docker_quickstart.md).
We have included multiple example files in `/llm/`: We have included multiple example files in `/llm/`:
1. `vllm_offline_inference.py`: Used for vLLM offline inference example 1. `vllm_offline_inference.py`: Used for vLLM offline inference example
@ -79,7 +79,7 @@ We have included multiple example files in `/llm/`:
##### Online benchmark throurgh api_server ##### Online benchmark throurgh api_server
We can benchmark the api_server to get an estimation about TPS (transactions per second). To do so, you need to start the service first according to the instructions in this [section](https://github.com/intel-analytics/ipex-llm/blob/main/python/llm/example/GPU/vLLM-Serving/README.md#service). We can benchmark the api_server to get an estimation about TPS (transactions per second). To do so, you need to start the service first according to the instructions in this [section](https://github.com/intel-analytics/ipex-llm/blob/main/docs/mddocs/DockerGuides/vllm_docker_quickstart.md#Serving).
###### Online benchmark through benchmark_util ###### Online benchmark through benchmark_util